Search Results for author:
Found 5 papers, 1 papers with code
WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses
To improve the accuracy and naturalness of synthesized singing voice, we design several specifical modules and techniques: 1) A deep bi-directional LSTM-based duration model with multi-scale rhythm loss and post-processing step; 2) A Transformer-alike acoustic model with progressive pitch-weighted decoder loss; 3) a 24 kHz pitch-aware LPCNet neural vocoder to produce high-quality singing waveforms; 4) A novel data augmentation method with multi-singer pre-training for stronger robustness and naturalness.
TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis
On one hand, we propose to discriminate ground-truth waveform from synthetic one in frequency domain for offering more consistency guarantees instead of only in time domain.
AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN
This paper investigates how to leverage a DurIAN-based average model to enable a new speaker to have both accurate pronunciation and fluent cross-lingual speaking with very limited monolingual data.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
We further show that applying deep residual learning can boost the convergence speed of our novel deep recurret convolutional networks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding
Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research.
